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Project 3: Data Interpretation

Project 3: Data Interpretation. Roger Woods, M.D. Parallels. Morpometric and Statistical Toolkit. New Aims. Tools for multivariate data reduction and model selection Adaptation of multivariate methods to non-Euclidean manifolds

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Project 3: Data Interpretation

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  1. Project 3: Data Interpretation • Roger Woods, M.D.

  2. Parallels

  3. Morpometric and Statistical Toolkit

  4. New Aims • Tools for multivariate data reduction and model selection • Adaptation of multivariate methods to non-Euclidean manifolds • Interoperability, intuitiveness, efficiency and user friendliness of statistical models

  5. Height: A “Simple” Problem Heritability: 80% (age and sex adjusted)

  6. Multivariate Height • Skull height • 7 Cervical vertebrae • 12 Thoracic vertebrae • 5 Lumbar vertebrae • Pelvic height • Upper leg length • Lower leg length • Infinite variations

  7. Globally Acting Genetic Sources of Variance

  8. Non-Global Genetic Sources

  9. Environmental Local Sources

  10. Brain Studies: The Height Problem in 2D, 3D or More Heritabilities up to 90%

  11. Global Influences Microcephaly

  12. Regional Influences Cerebellar Hypoplasia Callosal Agenesis

  13. Multivariate Data Reduction and Model Selection • Multivariate regression • Partial least squares and variants • ICA/PCA and variants • Model averaging • Linear discriminant analysis

  14. Blind Source Separation by ICA

  15. Mixed ICA/PCA • Models Subgaussian, Supergaussianand Gaussian Sources • Model Selection Using Leave-One-Out Cross Validation (Akaike Information Criterion is not valid) • Implemented as C code with interfaces in Matlab and in R • User-specified Known Sources • Parallelization High Priority

  16. ICA/PCA: Fetal Alcohol

  17. Adaptation of Multivariate Methods to non-Euclidean Manifolds • Model Selection on Shape Manifolds • All multivariate methods in Aim 1 • Diffusion Imaging • Selecting correct model versus model best supported by the data

  18. Multivariate Shape Manifolds

  19. ICA/PCA: Corpus Callosum Shape in Schizophrenia

  20. DTI Maximum Likelihood Analyses • Gaussian model recovers orientation of simulated primary diffusion axis better than Rician model, even when data are generated as Rician • The Cholesky-based approach for assuring positive-definite tensors often fails to converge to the true minimum

  21. Interoperability, Intuitiveness, Efficiency and User Friendliness • Support volumetric data, tracts, vectors, tensors • GUI’s for model specification • Multicore processor support • Spatial provenance • Touchstone pipelines

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